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BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations
[Image: see text] Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082612/ https://www.ncbi.nlm.nih.gov/pubmed/35467875 http://dx.doi.org/10.1021/acs.jpclett.2c00654 |
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author | Lier, Bettina Poliak, Peter Marquetand, Philipp Westermayr, Julia Oostenbrink, Chris |
author_facet | Lier, Bettina Poliak, Peter Marquetand, Philipp Westermayr, Julia Oostenbrink, Chris |
author_sort | Lier, Bettina |
collection | PubMed |
description | [Image: see text] Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (BuRNN), an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex. |
format | Online Article Text |
id | pubmed-9082612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-90826122022-05-10 BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations Lier, Bettina Poliak, Peter Marquetand, Philipp Westermayr, Julia Oostenbrink, Chris J Phys Chem Lett [Image: see text] Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (BuRNN), an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex. American Chemical Society 2022-04-25 2022-05-05 /pmc/articles/PMC9082612/ /pubmed/35467875 http://dx.doi.org/10.1021/acs.jpclett.2c00654 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Lier, Bettina Poliak, Peter Marquetand, Philipp Westermayr, Julia Oostenbrink, Chris BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations |
title | BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding
Neural Network/Molecular Mechanics Simulations |
title_full | BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding
Neural Network/Molecular Mechanics Simulations |
title_fullStr | BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding
Neural Network/Molecular Mechanics Simulations |
title_full_unstemmed | BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding
Neural Network/Molecular Mechanics Simulations |
title_short | BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding
Neural Network/Molecular Mechanics Simulations |
title_sort | burnn: buffer region neural network approach for polarizable-embedding
neural network/molecular mechanics simulations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082612/ https://www.ncbi.nlm.nih.gov/pubmed/35467875 http://dx.doi.org/10.1021/acs.jpclett.2c00654 |
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